In this work, we propose a data-driven health monitoring method for wind power generators, which learns an empirical model from the time-series sensor data and detects irregularities or faults in the turbines and blades. Our main objective is to predict any symptoms of faults as early as possible before the generators fall into malfunction. The data obtained from the wind power generators are strongly correlated multidimensional time-series with multiple states. In this study, we take such features into account and develop the probabilistic model for them, namely, hidden Markov and probabilistic principal component analysis. Once the model is learned with the data that contain no faulty events, it can be used to detect faults in new data by comparing the original sensor values and reconstructed ones. In this research, we apply this method to synthetic data and real-world wind turbine data and show the results of experiments to confirm the availability of the proposed method.
Chandola, V., Banerjee, A. and Kumar, V. (2009). Anomaly Detection: A Survey. ACM Computing Surveys. vol. 41(3). doi:10.1145/1541880.1541882
Forney, G. D. (1973). The Viterbi Algorithm. Proceedings of the IEEE. Vol. 61(3), pp. 268–278. doi:10.1109/PROC.1973.9030
Tipping, M. E. and Bishop, C. M. (1999). Mixtures of Probabilistic Principal Component Analysers. Neural Computation. vol. 11(2), pp. 443–482. doi:10.1162/08997669930001672